Visualisation Practice

#Correloplot
head(iris)

corriris <- iris
corriris$Species <- as.integer(corriris$Species)
corr <- round(cor(corriris),2)
ggcorrplot(corr, method = "circle",  lab = TRUE, hc.order = TRUE)

Using the correlation matrix, we begin to see high correlations amongst many categories. We’ll inspect the Length Vs Width first. I expect there to be a reasonably standardised positive correlation on the scatte graph based on this data.

plot_ly(
  x = iris$Sepal.Width,
  y = iris$Sepal.Length,
  name = iris$Species,
  type = "scatter",
  symbol = iris$Species
)

No scatter mode specifed:
  Setting the mode to markers
  Read more about this attribute -> https://plot.ly/r/reference/#scatter-mode
No scatter mode specifed:
  Setting the mode to markers
  Read more about this attribute -> https://plot.ly/r/reference/#scatter-mode

#Scatter Plots (Base R)
qplot(iris$Petal.Length, iris$Petal.Width, colour=iris$Species)

qplot(iris$Petal.Length, iris$Sepal.Length, colour=iris$Species)

qplot(iris$Petal.Width, iris$Sepal.Length, colour=iris$Species)

#Histogram
ggplot(iris, aes(x=Petal.Width)) + 
  geom_histogram(color="White", fill="Red", binwidth=0.3)